Hugging Face's logo

xXiaobuding
/
roberta-base_ai4privacy_en

Token Classification
Transformers
PyTorch
roberta
Generated from Trainer
Inference Endpoints
Model card Files Community
xXiaobuding commited on
Commit
0e7cf6c
•
1 Parent(s): 72b1437

update model card README.md

Browse files
Files changed (1) hide show
  1. README.md +121 -0
README.md ADDED
@@ -0,0 +1,121 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: mit
3
+ tags:
4
+ - generated_from_trainer
5
+ model-index:
6
+ - name: roberta-base_ai4privacy_en
7
+ results: []
8
+ ---
9
+
10
+ <!-- This model card has been generated automatically according to the information the Trainer had access to. You
11
+ should probably proofread and complete it, then remove this comment. -->
12
+
13
+ # roberta-base_ai4privacy_en
14
+
15
+ This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
16
+ It achieves the following results on the evaluation set:
17
+ - Loss: 0.0962
18
+ - Overall Precision: 0.8739
19
+ - Overall Recall: 0.9046
20
+ - Overall F1: 0.8890
21
+ - Overall Accuracy: 0.9623
22
+ - Accountname F1: 0.9898
23
+ - Accountnumber F1: 0.9896
24
+ - Age F1: 0.8745
25
+ - Amount F1: 0.8663
26
+ - Bic F1: 0.8782
27
+ - Bitcoinaddress F1: 0.9414
28
+ - Buildingnumber F1: 0.8279
29
+ - City F1: 0.8312
30
+ - Companyname F1: 0.9434
31
+ - County F1: 0.9279
32
+ - Creditcardcvv F1: 0.8947
33
+ - Creditcardissuer F1: 0.9755
34
+ - Creditcardnumber F1: 0.8770
35
+ - Currency F1: 0.6753
36
+ - Currencycode F1: 0.6398
37
+ - Currencyname F1: 0.2105
38
+ - Currencysymbol F1: 0.9223
39
+ - Date F1: 0.8276
40
+ - Dob F1: 0.5470
41
+ - Email F1: 0.9840
42
+ - Ethereumaddress F1: 0.9972
43
+ - Eyecolor F1: 0.9027
44
+ - Firstname F1: 0.8696
45
+ - Gender F1: 0.9627
46
+ - Height F1: 0.9811
47
+ - Iban F1: 0.9912
48
+ - Ip F1: 0.0124
49
+ - Ipv4 F1: 0.8377
50
+ - Ipv6 F1: 0.7585
51
+ - Jobarea F1: 0.8212
52
+ - Jobtitle F1: 0.9833
53
+ - Jobtype F1: 0.9110
54
+ - Lastname F1: 0.8305
55
+ - Litecoinaddress F1: 0.8793
56
+ - Mac F1: 0.9957
57
+ - Maskednumber F1: 0.8315
58
+ - Middlename F1: 0.9441
59
+ - Nearbygpscoordinate F1: 0.9970
60
+ - Ordinaldirection F1: 0.9682
61
+ - Password F1: 0.9654
62
+ - Phoneimei F1: 0.9944
63
+ - Phonenumber F1: 0.9860
64
+ - Pin F1: 0.8150
65
+ - Prefix F1: 0.9306
66
+ - Secondaryaddress F1: 0.9935
67
+ - Sex F1: 0.9721
68
+ - Ssn F1: 0.9759
69
+ - State F1: 0.8817
70
+ - Street F1: 0.8264
71
+ - Time F1: 0.9485
72
+ - Url F1: 0.9936
73
+ - Useragent F1: 0.9976
74
+ - Username F1: 0.9108
75
+ - Vehiclevin F1: 0.9568
76
+ - Vehiclevrm F1: 0.9239
77
+ - Zipcode F1: 0.8543
78
+
79
+ ## Model description
80
+
81
+ More information needed
82
+
83
+ ## Intended uses & limitations
84
+
85
+ More information needed
86
+
87
+ ## Training and evaluation data
88
+
89
+ More information needed
90
+
91
+ ## Training procedure
92
+
93
+ ### Training hyperparameters
94
+
95
+ The following hyperparameters were used during training:
96
+ - learning_rate: 5e-05
97
+ - train_batch_size: 16
98
+ - eval_batch_size: 32
99
+ - seed: 42
100
+ - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
101
+ - lr_scheduler_type: cosine_with_restarts
102
+ - lr_scheduler_warmup_ratio: 0.2
103
+ - num_epochs: 5
104
+
105
+ ### Training results
106
+
107
+ | Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy | Accountname F1 | Accountnumber F1 | Age F1 | Amount F1 | Bic F1 | Bitcoinaddress F1 | Buildingnumber F1 | City F1 | Companyname F1 | County F1 | Creditcardcvv F1 | Creditcardissuer F1 | Creditcardnumber F1 | Currency F1 | Currencycode F1 | Currencyname F1 | Currencysymbol F1 | Date F1 | Dob F1 | Email F1 | Ethereumaddress F1 | Eyecolor F1 | Firstname F1 | Gender F1 | Height F1 | Iban F1 | Ip F1 | Ipv4 F1 | Ipv6 F1 | Jobarea F1 | Jobtitle F1 | Jobtype F1 | Lastname F1 | Litecoinaddress F1 | Mac F1 | Maskednumber F1 | Middlename F1 | Nearbygpscoordinate F1 | Ordinaldirection F1 | Password F1 | Phoneimei F1 | Phonenumber F1 | Pin F1 | Prefix F1 | Secondaryaddress F1 | Sex F1 | Ssn F1 | State F1 | Street F1 | Time F1 | Url F1 | Useragent F1 | Username F1 | Vehiclevin F1 | Vehiclevrm F1 | Zipcode F1 |
108
+ |:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:--------------:|:----------:|:----------------:|:--------------:|:----------------:|:------:|:---------:|:------:|:-----------------:|:-----------------:|:-------:|:--------------:|:---------:|:----------------:|:-------------------:|:-------------------:|:-----------:|:---------------:|:---------------:|:-----------------:|:-------:|:------:|:--------:|:------------------:|:-----------:|:------------:|:---------:|:---------:|:-------:|:------:|:-------:|:-------:|:----------:|:-----------:|:----------:|:-----------:|:------------------:|:------:|:---------------:|:-------------:|:----------------------:|:-------------------:|:-----------:|:------------:|:--------------:|:------:|:---------:|:-------------------:|:------:|:------:|:--------:|:---------:|:-------:|:------:|:------------:|:-----------:|:-------------:|:-------------:|:----------:|
109
+ | 0.3911 | 1.0 | 2175 | 0.2642 | 0.6000 | 0.6420 | 0.6203 | 0.9119 | 0.9177 | 0.8043 | 0.6417 | 0.2664 | 0.4444 | 0.7762 | 0.2639 | 0.3614 | 0.6320 | 0.5282 | 0.5097 | 0.8493 | 0.4381 | 0.2180 | 0.0 | 0.0 | 0.4754 | 0.6817 | 0.0 | 0.9518 | 0.9710 | 0.5435 | 0.5869 | 0.6865 | 0.5455 | 0.7382 | 0.0 | 0.7780 | 0.7260 | 0.3064 | 0.6099 | 0.5096 | 0.4491 | 0.5872 | 0.8521 | 0.4547 | 0.0365 | 0.9822 | 0.7915 | 0.7728 | 0.9553 | 0.8337 | 0.1159 | 0.8853 | 0.8930 | 0.9520 | 0.7916 | 0.1731 | 0.2881 | 0.7877 | 0.9793 | 0.9202 | 0.6108 | 0.7374 | 0.4551 | 0.5222 |
110
+ | 0.1748 | 2.0 | 4350 | 0.1410 | 0.7732 | 0.8035 | 0.7880 | 0.9479 | 0.9831 | 0.9575 | 0.8131 | 0.6771 | 0.8161 | 0.8795 | 0.6822 | 0.6022 | 0.8531 | 0.6954 | 0.8056 | 0.9663 | 0.8012 | 0.5330 | 0.3009 | 0.0571 | 0.8293 | 0.7760 | 0.3798 | 0.9646 | 0.9675 | 0.8677 | 0.6901 | 0.9239 | 0.9655 | 0.9073 | 0.0 | 0.8345 | 0.7913 | 0.7190 | 0.9331 | 0.8958 | 0.5220 | 0.7748 | 0.9913 | 0.7372 | 0.4010 | 0.9925 | 0.9558 | 0.8982 | 0.9359 | 0.9586 | 0.6094 | 0.8621 | 0.9891 | 0.9702 | 0.9464 | 0.5906 | 0.5943 | 0.9437 | 0.9936 | 0.9369 | 0.8300 | 0.9157 | 0.8207 | 0.7405 |
111
+ | 0.1081 | 3.0 | 6525 | 0.1143 | 0.8376 | 0.8825 | 0.8595 | 0.9559 | 0.9865 | 0.9803 | 0.8586 | 0.7828 | 0.8154 | 0.8297 | 0.7920 | 0.7957 | 0.9143 | 0.8413 | 0.8218 | 0.9628 | 0.8634 | 0.6290 | 0.5636 | 0.1324 | 0.8788 | 0.8283 | 0.4895 | 0.9797 | 0.9917 | 0.8895 | 0.8303 | 0.9294 | 0.9718 | 0.9746 | 0.0 | 0.8325 | 0.7976 | 0.7521 | 0.9647 | 0.9140 | 0.7495 | 0.7371 | 0.9848 | 0.7944 | 0.8836 | 0.9955 | 0.9701 | 0.9227 | 0.9944 | 0.9785 | 0.7427 | 0.9254 | 0.9924 | 0.9701 | 0.9527 | 0.8031 | 0.7519 | 0.9288 | 0.9929 | 0.9848 | 0.8880 | 0.9391 | 0.9251 | 0.8403 |
112
+ | 0.0804 | 4.0 | 8700 | 0.0962 | 0.8739 | 0.9046 | 0.8890 | 0.9623 | 0.9898 | 0.9896 | 0.8745 | 0.8663 | 0.8782 | 0.9414 | 0.8279 | 0.8312 | 0.9434 | 0.9279 | 0.8947 | 0.9755 | 0.8770 | 0.6753 | 0.6398 | 0.2105 | 0.9223 | 0.8276 | 0.5470 | 0.9840 | 0.9972 | 0.9027 | 0.8696 | 0.9627 | 0.9811 | 0.9912 | 0.0124 | 0.8377 | 0.7585 | 0.8212 | 0.9833 | 0.9110 | 0.8305 | 0.8793 | 0.9957 | 0.8315 | 0.9441 | 0.9970 | 0.9682 | 0.9654 | 0.9944 | 0.9860 | 0.8150 | 0.9306 | 0.9935 | 0.9721 | 0.9759 | 0.8817 | 0.8264 | 0.9485 | 0.9936 | 0.9976 | 0.9108 | 0.9568 | 0.9239 | 0.8543 |
113
+ | 0.0663 | 5.0 | 10875 | 0.0965 | 0.8761 | 0.9089 | 0.8922 | 0.9632 | 0.9882 | 0.9896 | 0.8845 | 0.8676 | 0.8750 | 0.9463 | 0.8280 | 0.8415 | 0.9482 | 0.9365 | 0.8954 | 0.9774 | 0.8897 | 0.6571 | 0.6773 | 0.2690 | 0.9217 | 0.8259 | 0.6135 | 0.9859 | 0.9972 | 0.9180 | 0.8840 | 0.9708 | 0.975 | 0.9667 | 0.1201 | 0.8109 | 0.7064 | 0.8298 | 0.9885 | 0.9265 | 0.8520 | 0.8844 | 0.9935 | 0.8523 | 0.9462 | 0.9985 | 0.9744 | 0.9682 | 0.9958 | 0.9881 | 0.8166 | 0.9333 | 0.9935 | 0.9721 | 0.9772 | 0.8889 | 0.8294 | 0.9632 | 0.9952 | 0.9976 | 0.9172 | 0.9538 | 0.9418 | 0.8638 |
114
+
115
+
116
+ ### Framework versions
117
+
118
+ - Transformers 4.26.1
119
+ - Pytorch 2.0.0.post200
120
+ - Datasets 2.10.1
121
+ - Tokenizers 0.13.3